Is it really feasible that ten years from now, data for the atmospheric physics of other worlds will be available by the terabyte? I am writing about the spectra of alien atmospheres coming in by the hundreds of worlds, with the data of a higher quality than currently possible. The answer is evidently 'yes' according to a recent report in EOS: Space Science Journal, p.7.
The gist of it is fairly simple:An upcoming study in Monthly Notices of the Royal Astronomical Society documented a machine learning algorithm against the current gold standard process for decoding exoplanet atmospheres, i.e. to see whether the algorithm could tackle this future big-data problem.
The current front-runner for best deciphering a planet’s atmospheric spectrum is called atmospheric retrieval. It uses statistical inference to calculate the likelihood that given an observed spectrum, an exoplanet’s atmosphere will possess a certain composition, temperature, level of cloud cover, and heat flow. In the latter one would want to look at, for example, Rayleigh scattering in concert with radiative transport in standard gray atmosphere models, e,g. looking at the applicable equation for radiative transport:
-dI/dt (1/k r ) = I – J
Where k is a mass scattering coefficient, r is the molecular density (e.g. in cloud cover) and J is the vector source function for a specific intensity I.
The technique has so far proven very reliable but can be computationally expensive. (No wonder considering what is demanded of it!)
The algorithm compares each artificial spectrum with the real one and chooses the closest match. This is a new and modified application of the random forest algorithm for exoplanetary atmospheres that was originally developed by astronomers at the University of Bern in Switzerland.
As exoplanet atmosphere research moves into the big-data era, machine learning will become an increasingly important research tool scientists should be trained to use, according to Nikku Madhusudhan.. Some graduate programs are already integrating more data science learning into students’ training. (Co-author Matthew Nixon also has doctorate work supported by one such program in the United Kingdom.)
This study adds to a growing effort by exoplanet scientists to find an efficient and more effective way to handle the upcoming deluge of atmospheric data. In the words of Daniel Angerhausen, an astrophysicist at ETH Zürich in Switzerland who was not involved with this research:
“It is great to see a growing group in the community using machine learning methods and cross-checking each other’s results and claims,”
Missions like JWST and ARIEL are first at bat, but Angerhausen is also thinking about missions that will come after those. Astronomers will need to strategize the most efficient ways to observe interesting targets. As Angerhausen added:
“This problem is predestined for a [machine learning] approach. A random forest approach is just the “tip of the iceberg” for algorithms to try."
“On the other hand,” Madhusudhan added, “it also needs to be recognized that while machine learning is a great research tool in various areas, there are also important areas of research where other numerical, statistical, and analytic approaches are more suitable for some important problems. Therefore, I believe the right balance needs to be met while integrating machine learning into graduate programs in the right research areas.”
In the words of Ingo Waldmann - an astrophysicist at University College London:
"Perhaps unsurprisingly, the more detailed the model, the longer it takes to compute its results. Today we are rapidly reaching a stage where our traditional techniques become too slow to compute these increasingly complex models."
Adding:
“Machine learning may never replace an atmospheric expert, but I’m certain that artificial intelligence will certainly play a role as a helping hand.”
Given human inputs will be inadequate to the task of recovering terabytes of data on extraterrestrial planetary atmospheres, this observation can be said to be spot on. What will be needed is the actual implementation of A.I. and its passing a number of major tests for analyzing exoplanet atmospheres.
See Also:
https://www.eurekalert.org/pub_releases/2020-02/uoc-lec022520.php
and:
https://arxiv.org/abs/1904.03190
And this lecture on exoplanet atmospheres::
https://www.youtube.com/watch?v=XaoceffJSKQ
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